Summary of Hidden Flaws Behind Expert-level Accuracy Of Multimodal Gpt-4 Vision in Medicine, by Qiao Jin et al.
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
by Qiao Jin, Fangyuan Chen, Yiliang Zhou, Ziyang Xu, Justin M. Cheung, Robert Chen, Ronald M. Summers, Justin F. Rousseau, Peiyun Ni, Marc J Landsman, Sally L. Baxter, Subhi J. Al’Aref, Yijia Li, Alex Chen, Josef A. Brejt, Michael F. Chiang, Yifan Peng, Zhiyong Lu
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary GPT-4V, a Generative Pre-trained Transformer 4 with Vision model, has been shown to outperform human physicians in medical challenge tasks. However, previous studies have primarily focused on the accuracy of multi-choice questions, neglecting other important aspects like rationales and multimodal reasoning. Our study aimed to fill this gap by analyzing GPT-4V’s image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges. We found that GPT-4V performed similarly to human physicians in terms of multi-choice accuracy (81.6% vs. 77.8%). Moreover, it excelled in cases where physicians incorrectly answered, with over 78% accuracy. Nevertheless, our results revealed that GPT-4V often presented flawed rationales when making correct final choices (35.5%), particularly in image comprehension (27.2%). Our findings underscore the need for further evaluation of GPT-4V’s rationales before integrating such multimodal AI models into clinical workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPT-4V is a computer program that can help doctors make diagnoses and decisions. It was tested against real doctors to see how well it could do. The test asked questions with pictures, like what’s wrong with this X-ray? GPT-4V did really well on the easy questions, but not as great on the harder ones. What’s important is that even when it got something right, its explanation might be wrong. This means we need to check how it makes decisions before we use it in hospitals. |
Keywords
» Artificial intelligence » Gpt » Recall » Transformer
